In the field of Geographic Information Systems (GISs), it is known to capture, analyse, manage, and present data associated with locations. In this regard, GISs are known to enrich cartographic information using database technology in order to generate information-rich cartographic data.
In relation to a digital map, it is known for producers of digital maps to generate map products based upon a master resource of geospatial data. In this respect, it is known to use a master digital map database containing a great deal of data concerning geospatial layers. Geospatial layers are a collection of similar objects that are defined by geometry and attribution, for example a street layer. The geometry is usually represented by a series of coordinates that define points along the outline of the object. Objects are generally defined by polygons, lines or points. An example of a polygonal object would be the outline of a political boundary such as a county. An example of a line object would be a street segment. A single point object could represent a city center or a point of interest. Attributes are fields in a geospatial dataset that define information associated with geospatial objects. In the context of a street layer, for example, attributes that could be associated with a street segment in the street layer of the geospatial dataset could be: street names, prefixes (such as North or South), suffixes (such as Blvd or Street) and/or speed limits to name but a few. If the street segment represents a city block, an additional attribute can be an address range.
Additional attribution that defines the source of information used to create either geometry or attribution and the quality of information and/or the age of the information are referred to as metadata. An example of metadata for a street layer would be positional accuracy in terms of accuracy in meters.
Clearly, in order to be able to provide digital maps of the highest quality, it is necessary to keep the underlying information used to create the digital maps up-to-date. In order to achieve this aim, data is collected in a number of ways, for example aerial photography, video logging using mobile mapping vans and conflation—the merging of localized geospatial datasets. The information generated by these acquisitions techniques is stored electronically and the stored information is known as a “resource”. Indeed, the initial data stored in the master digital map database is typically derived from such resources. Clearly, as time progresses, the geospatial dataset becomes out-of-date and inaccurate, for example as new roads are built and underlying attribution changes. It is therefore necessary to update a geospatial database used to generate a digital map periodically from new resources.
New resources may only be useful to update specific attribution or geometry. For example, an aerial photograph may depict road geometry accurately, but may be of little utility value as a source of information for speed limit signs, because the signs deployed along a road network cannot be seen from the air. In contrast, video logs generated using mobile mapping vans can capture speed limit information very well. Furthermore, within a resource the quality of the information content can vary. For example, an aerial photograph of a desert can show road geometry very clearly, whereas an aerial photograph of a forest may not show any or only some roads due to visibility of the roads from the air being obscured by trees.
Additionally, quality of the information content contained by a resource can change with time. For example, a resource that was 100% complete and accurate at a first point in time for street names in a given geographic area can diminish in completeness and accuracy over time and at a second, later, point in time, can be less accurate and complete due to construction of new streets, street renaming and other real-world changes. Consequently, a resource that is 100% complete and accurate at the time recorded, may be much less complete and accurate ten years later and no longer an optimum choice for use as a resource as compared with another resource that is only 92% complete and accurate, but more recently recorded.
In order to edit a geospatial dataset, so-called map editor applications are employed. A map editor application uses resources of the type mentioned above in order to update information content relating to geometry and/or attribution stored as part of the geospatial dataset. When an aerial photograph becomes available showing new roads that are not yet recorded in the geospatial dataset, the geometry of the roads in question are added to the geospatial dataset using the map editor application.
In addition to editing, automated processes are also used to update the geospatial dataset. An example of this is the process known as conflation mentioned above, where a particular dataset for a specific geographic area is merged into the master dataset in an automated fashion. For example, a geospatial dataset maintained by a county government and containing a street layer can be merged into a state database, because the geospatial dataset maintained by the county government is of superior quality to a state database for the state of which the county is part. Automated conflation algorithms attempt to merge the one or more superior quality aspects of the county database with the existing information of the master state database.
A number of difficulties exist when editing the information content associated with attribution or geometry of a geospatial dataset of the master digital map database.
One particular problem relates to choice of resource to use. In this respect, more than one resource may be available from which content information for a given attribute of the geospatial dataset can be obtained. However, information content obtained from one resource in respect of one attribute or a geometry of the geospatial dataset may not be an optimum resource for the information content of a different type of attribute or geometry of the geospatial dataset. Also, information content obtained from one resource in respect of an instance of an attribute or geometry of the geospatial dataset does not mean that the same resource is an optimum resource for another instance of the same type of attribute or geometry of the geospatial dataset. Similarly, as suggested above, a resource may be an optimum resource from which to obtain information content for an attribute or geometry at one point in time, but may not be the optimum resource from which to obtain the information content at a later point in time. Consequently, a most recently dated resource of a given type may not necessarily be the optimum resource from which to obtain the information content. For example, a resource of earlier date, but of superior quality, may be the optimum resource to use in preference over a more recent resource. Likewise, if the more recent resource comprises information content relating to the attribute, but was collected using a lower quality method than an older resource, the older resource may still be the optimum resource to use.
In order to record quality of a resource used in order to edit or initially record an attribute or geometry of a geographic feature, some current implementations of geospatial datasets simply comprise flags or indicators of the quality of an attribute or geometry. However, this technique, an example of the “additional attribution” mentioned above, can lead to a number of disadvantages. Firstly, simple recordal of flags or indicators of quality in respect of attributes or geometries in the geospatial dataset of the master digital map database does not enable comparison of different resources to take place and use of a resource less accurate than one already used would result in degradation in the quality of the current geospatial dataset. Secondly, when a new resource becomes available and is applicable in respect of a number of attributes or geometries, an editor application or a conflation application is unable to identify all attributes or geometry having information content obtained from an existing resource that needs to be replaced. Similarly, it is not possible to determine when it is necessary to override a currently used resource with a different resource.
Many geospatial datasets also contain metadata (data about data) which generally describe the overall currency, precision and accuracy of source material of a geospatial dataset, but rarely do these geospatial datasets comprise information to the level of individual geometric objects or individual attributes associated with those objects. In addition, there is no clearly defined ranking of the quality of the source relative to the other different types of sources. Therefore no clear approach can be taken as to when aspects of the geospatial dataset need to be updated.
Also, as suggested above, the confidence in a resource as a source of the information content degrades with time as does confidence in the actual information content used to characterise an attribute in the current geospatial dataset. For example, a road network shown in a video log captured by a mobile mapping van is perfectly accurate on the day of capture, but becomes less accurate over time as construction or other real-world changes occur. Furthermore, different resources degrade at different rates. For example, a resource recording a road network in a city is likely to remain relatively stable over time compared to another resource recording a road network in a growing suburb. Additionally, for a given resource, the information content for different attributes can degrade at different rates. For example, a road network in a city can remain relatively stable over time, but navigation attributes, such as one-way designations, can change relatively frequently.
In relation to resource selection, it is known to select manually an optimum resource from among a number of candidates, and sometimes conflicting, resources for editing a current geospatial dataset based upon a single criterion, for example date of capture. However, such an approach neither takes account of the inherent quality of a resource for a particular attribute to be edited nor a variance of quality of an attribute within the resource. Alternatively, simple rules are known to be assigned for the use of certain types of resource, for example aerial photographs are not used for extraction of sign text, because signs are not visible from the air. However, application of such simple rules does not take into account the currency of the resource.
When updating an attribute, where multiple factors are to be taken into account when assessing resources, it is known to apply heuristics. For example, a “least recently maintained” algorithm can be used to identify attributes of a geospatial dataset in need of updating. However, heuristics typically only take simple conditions into account and so are naïve and/or are slow to implement in a map editing environment. Consequently, the use of heuristics can lead to relevant factors being ignored and/or are not cost effective to implement. Furthermore, the application of heuristics by manual methods is subject to human error.